Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 431–440 of 703 papers

Title	Date	Tasks	Status	Hype
In search of the weirdest galaxies in the Universe	Jul 16, 2020	ClusteringDimensionality Reduction	CodeCode Available	0
Generic Outlier Detection in Multi-Armed Bandit	Jul 14, 2020	Outlier Detection	—Unverified	0
It Is Likely That Your Loss Should be a Likelihood	Jul 12, 2020	Outlier Detection	—Unverified	0
Learning low-dimensional manifolds under the L0-norm constraint for unsupervised outlier detection	Jul 9, 2020	Outlier Detection	—Unverified	0
Deep Learning for Anomaly Detection: A Review	Jul 6, 2020	Anomaly DetectionDeep Learning	—Unverified	0
Variational Autoencoders for Anomalous Jet Tagging	Jul 3, 2020	Jet TaggingOutlier Detection	CodeCode Available	0
Explainable Deep One-Class Classification	Jul 3, 2020	Anomaly DetectionClassification	CodeCode Available	1
Outlier Detection through Null Space Analysis of Neural Networks	Jul 2, 2020	ClassificationGeneral Classification	—Unverified	0
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification	Jul 1, 2020	ClassificationGeneral Classification	CodeCode Available	1
Practical applications of metric space magnitude and weighting vectors	Jun 24, 2020	Active LearningBoundary Detection	—Unverified	0

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All datasets ECG5000 Balance scale_class 1 Breast cancer Wisconsin_class 2 Breast cancer Wisconsin_class 4 Fashion-MNIST Glass identification Heart-C Hepatitis Internet Ad Ionosphere_class b SKAB

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	VRAE+SVM	Accuracy	0.98	—	Unverified
2	F-t ALSTM-FCN	Accuracy	0.95	—	Unverified
3	GENDIS	Accuracy	0.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	99.03	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	37.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	65.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PAE	AUROC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	99.05	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC	0.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC-ROC	0.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC-ROC	0.93	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	86.33	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSTMCaps	Average F1	0.74	—	Unverified